Institutional Trust
#Design
mergesvy <- mergeiccs %>%
as_survey_design(
strata = jkzones,
weights = totwgts,
ids = jkreps,
nest = TRUE)
#Adjust and table
mergesvy[["variables"]][["nac_gob_d"]] <- as.character(mergesvy[["variables"]][["nac_gob_d"]])
mergesvy[["variables"]][["police_d"]] <- as.character(mergesvy[["variables"]][["police_d"]])
mergesvy[["variables"]][["pol_parties_d"]] <- as.character(mergesvy[["variables"]][["pol_parties_d"]])
mergesvy[["variables"]][["people_d"]] <- as.character(mergesvy[["variables"]][["people_d"]])
#########################
#National Goberment
#########################
table_freq_01 <- mergesvy %>%
dplyr::group_by(country, time, nac_gob_d) %>%
summarize(proportion = survey_mean(,na.rm=TRUE, "ci"))
#Table
#print(xtable(table_freq_01, caption = "Sample", format="text"), include.rownames=FALSE)
kable(table_freq_01, align = c("lcccccc")) %>%
kable_styling(bootstrap_options = c("striped", "hover"))
|
country
|
time
|
nac_gob_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.3486123
|
0.3291055
|
0.3681191
|
|
CHL
|
2009
|
1
|
0.6513877
|
0.6318809
|
0.6708945
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.5041627
|
0.4847861
|
0.5235392
|
|
CHL
|
2016
|
1
|
0.4958373
|
0.4764608
|
0.5152139
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.3797472
|
0.3552204
|
0.4042741
|
|
COL
|
2009
|
1
|
0.6202528
|
0.5957259
|
0.6447796
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.4476026
|
0.4236968
|
0.4715084
|
|
COL
|
2016
|
1
|
0.5523974
|
0.5284916
|
0.5763032
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.2612638
|
0.2365092
|
0.2860184
|
|
DOM
|
2009
|
1
|
0.7387362
|
0.7139816
|
0.7634908
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.2219649
|
0.1992088
|
0.2447210
|
|
DOM
|
2016
|
1
|
0.7780351
|
0.7552790
|
0.8007912
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.5469156
|
0.5200227
|
0.5738086
|
|
GTM
|
2009
|
1
|
0.4530844
|
0.4261914
|
0.4799773
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.4158023
|
0.3954032
|
0.4362014
|
|
MEX
|
2009
|
1
|
0.5841977
|
0.5637986
|
0.6045968
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.4299568
|
0.4090000
|
0.4509136
|
|
MEX
|
2016
|
1
|
0.5700432
|
0.5490864
|
0.5910000
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.5096989
|
0.4893269
|
0.5300710
|
|
PER
|
2016
|
1
|
0.4903011
|
0.4699290
|
0.5106731
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.3409936
|
0.3156542
|
0.3663329
|
|
PRY
|
2009
|
1
|
0.6590064
|
0.6336671
|
0.6843458
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
country
|
time
|
police_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.2896503
|
0.2717131
|
0.3075874
|
|
CHL
|
2009
|
1
|
0.7103497
|
0.6924126
|
0.7282869
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.3551635
|
0.3366103
|
0.3737167
|
|
CHL
|
2016
|
1
|
0.6448365
|
0.6262833
|
0.6633897
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.4507952
|
0.4293250
|
0.4722654
|
|
COL
|
2009
|
1
|
0.5492048
|
0.5277346
|
0.5706750
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.5076088
|
0.4828832
|
0.5323344
|
|
COL
|
2016
|
1
|
0.4923912
|
0.4676656
|
0.5171168
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.4369578
|
0.4107802
|
0.4631354
|
|
DOM
|
2009
|
1
|
0.5630422
|
0.5368646
|
0.5892198
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.4393891
|
0.4152856
|
0.4634927
|
|
DOM
|
2016
|
1
|
0.5606109
|
0.5365073
|
0.5847144
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.6664363
|
0.6423384
|
0.6905341
|
|
GTM
|
2009
|
1
|
0.3335637
|
0.3094659
|
0.3576616
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.5699461
|
0.5528957
|
0.5869966
|
|
MEX
|
2009
|
1
|
0.4300539
|
0.4130034
|
0.4471043
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.5097115
|
0.4933779
|
0.5260450
|
|
MEX
|
2016
|
1
|
0.4902885
|
0.4739550
|
0.5066221
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.4978979
|
0.4810746
|
0.5147213
|
|
PER
|
2016
|
1
|
0.5021021
|
0.4852787
|
0.5189254
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.5494438
|
0.5270717
|
0.5718159
|
|
PRY
|
2009
|
1
|
0.4505562
|
0.4281841
|
0.4729283
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
country
|
time
|
pol_parties_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.6550144
|
0.6346769
|
0.6753518
|
|
CHL
|
2009
|
1
|
0.3449856
|
0.3246482
|
0.3653231
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.6745075
|
0.6587782
|
0.6902368
|
|
CHL
|
2016
|
1
|
0.3254925
|
0.3097632
|
0.3412218
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.6506877
|
0.6295206
|
0.6718549
|
|
COL
|
2009
|
1
|
0.3493123
|
0.3281451
|
0.3704794
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.7228073
|
0.7029239
|
0.7426906
|
|
COL
|
2016
|
1
|
0.2771927
|
0.2573094
|
0.2970761
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.4887925
|
0.4644688
|
0.5131161
|
|
DOM
|
2009
|
1
|
0.5112075
|
0.4868839
|
0.5355312
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.5024324
|
0.4798019
|
0.5250629
|
|
DOM
|
2016
|
1
|
0.4975676
|
0.4749371
|
0.5201981
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.7368829
|
0.7169853
|
0.7567805
|
|
GTM
|
2009
|
1
|
0.2631171
|
0.2432195
|
0.2830147
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.6535926
|
0.6329986
|
0.6741866
|
|
MEX
|
2009
|
1
|
0.3464074
|
0.3258134
|
0.3670014
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.6261329
|
0.6059780
|
0.6462878
|
|
MEX
|
2016
|
1
|
0.3738671
|
0.3537122
|
0.3940220
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.6660155
|
0.6471337
|
0.6848973
|
|
PER
|
2016
|
1
|
0.3339845
|
0.3151027
|
0.3528663
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.6755932
|
0.6567009
|
0.6944855
|
|
PRY
|
2009
|
1
|
0.3244068
|
0.3055145
|
0.3432991
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
country
|
time
|
people_d
|
proportion
|
proportion_low
|
proportion_upp
|
|
CHL
|
2009
|
0
|
0.4825848
|
0.4652249
|
0.4999446
|
|
CHL
|
2009
|
1
|
0.5174152
|
0.5000554
|
0.5347751
|
|
CHL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
CHL
|
2016
|
0
|
0.5222508
|
0.5053029
|
0.5391987
|
|
CHL
|
2016
|
1
|
0.4777492
|
0.4608013
|
0.4946971
|
|
CHL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2009
|
0
|
0.5116344
|
0.4933059
|
0.5299628
|
|
COL
|
2009
|
1
|
0.4883656
|
0.4700372
|
0.5066941
|
|
COL
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
COL
|
2016
|
0
|
0.5651447
|
0.5430788
|
0.5872106
|
|
COL
|
2016
|
1
|
0.4348553
|
0.4127894
|
0.4569212
|
|
COL
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2009
|
0
|
0.3897426
|
0.3636206
|
0.4158645
|
|
DOM
|
2009
|
1
|
0.6102574
|
0.5841355
|
0.6363794
|
|
DOM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
DOM
|
2016
|
0
|
0.3801493
|
0.3586919
|
0.4016067
|
|
DOM
|
2016
|
1
|
0.6198507
|
0.5983933
|
0.6413081
|
|
DOM
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
GTM
|
2009
|
0
|
0.5258643
|
0.5041887
|
0.5475399
|
|
GTM
|
2009
|
1
|
0.4741357
|
0.4524601
|
0.4958113
|
|
GTM
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2009
|
0
|
0.5341219
|
0.5191580
|
0.5490859
|
|
MEX
|
2009
|
1
|
0.4658781
|
0.4509141
|
0.4808420
|
|
MEX
|
2009
|
NA
|
NA
|
NA
|
NA
|
|
MEX
|
2016
|
0
|
0.4829065
|
0.4638750
|
0.5019381
|
|
MEX
|
2016
|
1
|
0.5170935
|
0.4980619
|
0.5361250
|
|
MEX
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PER
|
2016
|
0
|
0.5284377
|
0.5113651
|
0.5455104
|
|
PER
|
2016
|
1
|
0.4715623
|
0.4544896
|
0.4886349
|
|
PER
|
2016
|
NA
|
NA
|
NA
|
NA
|
|
PRY
|
2009
|
0
|
0.4276067
|
0.4082165
|
0.4469968
|
|
PRY
|
2009
|
1
|
0.5723933
|
0.5530032
|
0.5917835
|
|
PRY
|
2009
|
NA
|
NA
|
NA
|
NA
|
OLS by chile
## Loading required package: mice
##
## Attaching package: 'mice'
## The following object is masked from 'package:tidyr':
##
## complete
## The following objects are masked from 'package:base':
##
## cbind, rbind
## * miceadds 3.5-14 (2019-08-23 13:23:33)
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=chi, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=chi, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=chi, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#htmlreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
72.44***
|
793.05***
|
792.12***
|
|
|
(0.71)
|
(56.41)
|
(56.36)
|
|
s_intrust
|
0.20***
|
0.18***
|
0.37***
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.05***
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.02
|
-0.02
|
|
|
|
(0.09)
|
(0.09)
|
|
s_homelit
|
|
-0.14
|
-0.14
|
|
|
|
(0.09)
|
(0.09)
|
|
s_gender
|
|
-0.67***
|
-0.67***
|
|
|
|
(0.18)
|
(0.18)
|
|
s_poldisc
|
|
0.01
|
0.01
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
-0.36***
|
-0.36***
|
|
|
|
(0.03)
|
(0.03)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00***
|
|
|
|
|
(0.00)
|
|
R2
|
0.36
|
0.38
|
0.38
|
|
Adj. R2
|
0.36
|
0.38
|
0.38
|
|
Num. obs.
|
10071
|
9855
|
9855
|
|
RMSE
|
62.81
|
62.23
|
62.18
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
56.49***
|
949.93***
|
|
|
(0.61)
|
(64.44)
|
|
civic_knowledge
|
-0.02***
|
-0.02***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.08
|
|
|
|
(0.11)
|
|
s_homelit
|
|
-0.21*
|
|
|
|
(0.10)
|
|
s_gender
|
|
-0.86***
|
|
|
|
(0.21)
|
|
s_poldisc
|
|
0.13***
|
|
|
|
(0.01)
|
|
time
|
|
-0.45***
|
|
|
|
(0.03)
|
|
R2
|
0.02
|
0.05
|
|
Adj. R2
|
0.02
|
0.05
|
|
Num. obs.
|
10089
|
9873
|
|
RMSE
|
73.29
|
71.94
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Colombia
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
col <- mergeiccs %>% filter(idcountry == 170)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=col, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=col, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=col, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#htmlreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
77.16***
|
-268.35***
|
-268.65***
|
|
|
(0.66)
|
(44.89)
|
(44.89)
|
|
s_intrust
|
0.10***
|
0.11***
|
0.16***
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.07***
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.07
|
0.07
|
|
|
|
(0.05)
|
(0.05)
|
|
s_homelit
|
|
-0.13
|
-0.13
|
|
|
|
(0.07)
|
(0.07)
|
|
s_gender
|
|
-1.28***
|
-1.28***
|
|
|
|
(0.15)
|
(0.15)
|
|
s_poldisc
|
|
-0.03***
|
-0.03***
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
0.17***
|
0.17***
|
|
|
|
(0.02)
|
(0.02)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.35
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
11233
|
11038
|
11038
|
|
RMSE
|
83.19
|
82.73
|
82.72
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
62.34***
|
258.87***
|
|
|
(0.59)
|
(55.83)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.24***
|
|
|
|
(0.06)
|
|
s_homelit
|
|
-0.24**
|
|
|
|
(0.09)
|
|
s_gender
|
|
-1.80***
|
|
|
|
(0.18)
|
|
s_poldisc
|
|
0.16***
|
|
|
|
(0.01)
|
|
time
|
|
-0.10***
|
|
|
|
(0.03)
|
|
R2
|
0.04
|
0.08
|
|
Adj. R2
|
0.04
|
0.08
|
|
Num. obs.
|
11262
|
11064
|
|
RMSE
|
105.09
|
103.12
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Dominican Republic
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
dom <- mergeiccs %>% filter(idcountry == 214)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=dom, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=dom, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=dom, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#htmlreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
74.97***
|
-169.07**
|
-169.51**
|
|
|
(0.85)
|
(60.43)
|
(60.43)
|
|
s_intrust
|
0.13***
|
0.12***
|
0.17**
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.07***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.03
|
0.03
|
|
|
|
(0.08)
|
(0.08)
|
|
s_homelit
|
|
-0.44***
|
-0.44***
|
|
|
|
(0.10)
|
(0.10)
|
|
s_gender
|
|
-0.85***
|
-0.85***
|
|
|
|
(0.21)
|
(0.21)
|
|
s_poldisc
|
|
-0.02
|
-0.02
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
0.12***
|
0.12***
|
|
|
|
(0.03)
|
(0.03)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.31
|
0.32
|
0.32
|
|
Adj. R2
|
0.31
|
0.32
|
0.32
|
|
Num. obs.
|
7117
|
6655
|
6655
|
|
RMSE
|
48.31
|
47.95
|
47.95
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
71.67***
|
-228.12**
|
|
|
(0.75)
|
(79.93)
|
|
civic_knowledge
|
-0.04***
|
-0.04***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.12
|
|
|
|
(0.10)
|
|
s_homelit
|
|
-0.17
|
|
|
|
(0.13)
|
|
s_gender
|
|
-1.58***
|
|
|
|
(0.27)
|
|
s_poldisc
|
|
0.09***
|
|
|
|
(0.01)
|
|
time
|
|
0.15***
|
|
|
|
(0.04)
|
|
R2
|
0.07
|
0.09
|
|
Adj. R2
|
0.07
|
0.09
|
|
Num. obs.
|
7160
|
6685
|
|
RMSE
|
64.17
|
63.62
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Mexico
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
mex <- mergeiccs %>% filter(idcountry == 484)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=mex, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=mex, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=mex, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#htmlreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
77.58***
|
-267.31***
|
-276.62***
|
|
|
(0.71)
|
(50.95)
|
(50.97)
|
|
s_intrust
|
0.20***
|
0.19***
|
0.39***
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.08***
|
-0.08***
|
-0.06***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.03
|
0.03
|
|
|
|
(0.06)
|
(0.06)
|
|
s_homelit
|
|
-0.21**
|
-0.22**
|
|
|
|
(0.08)
|
(0.08)
|
|
s_gender
|
|
-1.05***
|
-1.05***
|
|
|
|
(0.17)
|
(0.17)
|
|
s_poldisc
|
|
-0.02*
|
-0.02*
|
|
|
|
(0.01)
|
(0.01)
|
|
time
|
|
0.17***
|
0.17***
|
|
|
|
(0.03)
|
(0.03)
|
|
s_intrust:civic_knowledge
|
|
|
-0.00***
|
|
|
|
|
(0.00)
|
|
R2
|
0.40
|
0.41
|
0.41
|
|
Adj. R2
|
0.40
|
0.41
|
0.41
|
|
Num. obs.
|
11583
|
11379
|
11379
|
|
RMSE
|
166.24
|
165.07
|
164.96
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
63.18***
|
-272.13***
|
|
|
(0.57)
|
(58.97)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.04
|
|
|
|
(0.07)
|
|
s_homelit
|
|
-0.34***
|
|
|
|
(0.09)
|
|
s_gender
|
|
-0.74***
|
|
|
|
(0.20)
|
|
s_poldisc
|
|
0.10***
|
|
|
|
(0.01)
|
|
time
|
|
0.16***
|
|
|
|
(0.03)
|
|
R2
|
0.05
|
0.06
|
|
Adj. R2
|
0.05
|
0.06
|
|
Num. obs.
|
11634
|
11426
|
|
RMSE
|
193.85
|
191.71
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
plot2

OLS by Guatemala 2009
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
gtm <- mergeiccs %>% filter(idcountry == 320)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=gtm, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=gtm, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=gtm, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#htmlreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
74.49***
|
75.83***
|
89.85***
|
|
|
(1.11)
|
(1.32)
|
(3.62)
|
|
s_intrust
|
0.11***
|
0.11***
|
-0.19*
|
|
|
(0.01)
|
(0.01)
|
(0.07)
|
|
civic_knowledge
|
-0.07***
|
-0.07***
|
-0.10***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.23**
|
0.26***
|
|
|
|
(0.08)
|
(0.08)
|
|
s_homelit
|
|
-0.13
|
-0.13
|
|
|
|
(0.11)
|
(0.11)
|
|
s_gender
|
|
-1.34***
|
-1.36***
|
|
|
|
(0.24)
|
(0.24)
|
|
s_poldisc
|
|
-0.00
|
0.00
|
|
|
|
(0.01)
|
(0.01)
|
|
s_intrust:civic_knowledge
|
|
|
0.00***
|
|
|
|
|
(0.00)
|
|
R2
|
0.35
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
3798
|
3684
|
3684
|
|
RMSE
|
41.28
|
41.24
|
41.15
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
65.52***
|
58.11***
|
|
|
(0.93)
|
(1.35)
|
|
civic_knowledge
|
-0.04***
|
-0.04***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.17
|
|
|
|
(0.10)
|
|
s_homelit
|
|
-0.26
|
|
|
|
(0.14)
|
|
s_gender
|
|
-0.74*
|
|
|
|
(0.30)
|
|
s_poldisc
|
|
0.12***
|
|
|
|
(0.02)
|
|
R2
|
0.10
|
0.12
|
|
Adj. R2
|
0.10
|
0.12
|
|
Num. obs.
|
3804
|
3690
|
|
RMSE
|
52.42
|
51.66
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

OLS by Paraguay 2009
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
pry <- mergeiccs %>% filter(idcountry == 604)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=pry, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=pry, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=pry, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#htmlreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
69.95***
|
71.15***
|
71.25***
|
|
|
(0.84)
|
(1.02)
|
(2.71)
|
|
s_intrust
|
0.10***
|
0.09***
|
0.09
|
|
|
(0.01)
|
(0.01)
|
(0.05)
|
|
civic_knowledge
|
-0.05***
|
-0.06***
|
-0.06***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.35***
|
0.35***
|
|
|
|
(0.08)
|
(0.08)
|
|
s_homelit
|
|
-0.04
|
-0.04
|
|
|
|
(0.10)
|
(0.10)
|
|
s_gender
|
|
-1.67***
|
-1.67***
|
|
|
|
(0.20)
|
(0.20)
|
|
s_poldisc
|
|
-0.00
|
-0.00
|
|
|
|
(0.01)
|
(0.01)
|
|
s_intrust:civic_knowledge
|
|
|
0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.35
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
5034
|
4914
|
4914
|
|
RMSE
|
69.71
|
69.44
|
69.45
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
63.12***
|
55.90***
|
|
|
(0.68)
|
(1.08)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.15
|
|
|
|
(0.11)
|
|
s_homelit
|
|
0.19
|
|
|
|
(0.14)
|
|
s_gender
|
|
-1.75***
|
|
|
|
(0.27)
|
|
s_poldisc
|
|
0.14***
|
|
|
|
(0.01)
|
|
R2
|
0.09
|
0.12
|
|
Adj. R2
|
0.09
|
0.11
|
|
Num. obs.
|
5037
|
4916
|
|
RMSE
|
92.75
|
91.50
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

OLS by Peru 2016
##########################################################3
#OLS
##########################################################3
#Borramos los modelos generados en el apartado anterior
rm(list=(ls()[!ls() %in% ("mergeiccs")]))
per <- mergeiccs %>% filter(idcountry == 600)
#VD: Support authoritarianism
m1 <- lm(l_autgov ~ s_intrust + civic_knowledge, data=per, w=totwgts)
m2 <- lm(l_autgov ~ s_intrust + civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)
m3 <- lm(l_autgov ~ s_intrust*civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)
#VD: Trust
m4 <- lm(s_intrust ~ civic_knowledge, data=per, w=totwgts)
m5 <- lm(s_intrust ~ civic_knowledge + s_hisced + s_homelit + s_gender + s_poldisc + time, data=per, w=totwgts)
#Table
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1, m2, m3, m4, m5), digits = 2)
#htmlreg(list(m1, m2, m3, m4, m5), digits = 4)
htmlreg(list(m1, m2, m3), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
70.30***
|
71.54***
|
76.89***
|
|
|
(1.19)
|
(1.40)
|
(4.16)
|
|
s_intrust
|
0.13***
|
0.10***
|
-0.01
|
|
|
(0.01)
|
(0.02)
|
(0.08)
|
|
civic_knowledge
|
-0.06***
|
-0.06***
|
-0.07***
|
|
|
(0.00)
|
(0.00)
|
(0.01)
|
|
s_hisced
|
|
0.15
|
0.15
|
|
|
|
(0.10)
|
(0.10)
|
|
s_homelit
|
|
-0.02
|
-0.01
|
|
|
|
(0.14)
|
(0.14)
|
|
s_gender
|
|
-1.40***
|
-1.41***
|
|
|
|
(0.28)
|
(0.28)
|
|
s_poldisc
|
|
0.01
|
0.01
|
|
|
|
(0.01)
|
(0.01)
|
|
s_intrust:civic_knowledge
|
|
|
0.00
|
|
|
|
|
(0.00)
|
|
R2
|
0.36
|
0.36
|
0.36
|
|
Adj. R2
|
0.35
|
0.36
|
0.36
|
|
Num. obs.
|
2958
|
2756
|
2756
|
|
RMSE
|
36.43
|
36.54
|
36.54
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
|
(Intercept)
|
63.22***
|
59.21***
|
|
|
(0.92)
|
(1.28)
|
|
civic_knowledge
|
-0.03***
|
-0.03***
|
|
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
0.05
|
|
|
|
(0.12)
|
|
s_homelit
|
|
0.23
|
|
|
|
(0.17)
|
|
s_gender
|
|
-2.32***
|
|
|
|
(0.34)
|
|
s_poldisc
|
|
0.10***
|
|
|
|
(0.02)
|
|
R2
|
0.07
|
0.10
|
|
Adj. R2
|
0.07
|
0.10
|
|
Num. obs.
|
2968
|
2766
|
|
RMSE
|
45.62
|
44.53
|
|
p < 0.001, p < 0.01, p < 0.05
|
plot2 <- plot_model(m3, type = "pred", terms = c("s_intrust[20,80]", "civic_knowledge[200, 300, 400, 500, 600, 700, 800]", "time"), colors = 'bw', axis.title = c("Students' trust in civic institutions", "Support for authoritarian practices"), title = c(""), legend.title = "Civic Knowledge")
## Warning in predict.lm(model, newdata = fitfram, type = "response", se.fit =
## se, : prediction from a rank-deficient fit may be misleading

Multilevel Models Aproximation: cluster by schools
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Warning: Some predictor variables are on very different scales: consider rescaling
## Warning: Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Warning: Some predictor variables are on very different scales: consider rescaling
## Warning: Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
## Warning: Some predictor variables are on very different scales: consider rescaling
## Warning: Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?
#m9b <- lmer(s_intrust ~ civic_knowledge*l_autgov + s_hisced + s_homelit + s_gender + s_poldisc + + (1|time) + (1|idcountry) + (1|idschool), data=mergeiccs, w=totwgts)
#Table
#library(texreg)
#print(xtable(iccs_count[, c(2:1,6,3:5)], caption = "Sample", format="text"), include.rownames=FALSE)
#texreg(list(m1b, m2b, m3b), digits = 2)
#texreg(list(m4b, m5b, m6b, m7b, m8b, m9b), digits = 2)
htmlreg(list(m1b, m2b, m3b), caption = "VD: Support authoritarianism")
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Support authoritarianism
|
|
Model 1
|
Model 2
|
Model 3
|
|
(Intercept)
|
85.56***
|
86.01***
|
85.63***
|
|
|
(0.24)
|
(0.31)
|
(0.35)
|
|
civic_knowledge
|
-0.08***
|
-0.08***
|
-0.08***
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
-0.05
|
0.04
|
|
|
|
(0.03)
|
(0.03)
|
|
s_homelit
|
|
-0.24***
|
-0.20***
|
|
|
|
(0.04)
|
(0.04)
|
|
s_gender
|
|
-1.38***
|
-1.32***
|
|
|
|
(0.08)
|
(0.08)
|
|
s_poldisc
|
|
0.00
|
-0.00
|
|
|
|
(0.00)
|
(0.00)
|
|
factor(time)2016
|
|
|
0.92***
|
|
|
|
|
(0.09)
|
|
factor(idcountry)170
|
|
|
0.80***
|
|
|
|
|
(0.17)
|
|
factor(idcountry)214
|
|
|
-0.58*
|
|
|
|
|
(0.25)
|
|
factor(idcountry)320
|
|
|
-0.09
|
|
|
|
|
(0.32)
|
|
factor(idcountry)484
|
|
|
0.50**
|
|
|
|
|
(0.15)
|
|
factor(idcountry)600
|
|
|
-0.73
|
|
|
|
|
(0.38)
|
|
factor(idcountry)604
|
|
|
0.14
|
|
|
|
|
(0.21)
|
|
AIC
|
420567.91
|
399727.19
|
399554.50
|
|
BIC
|
420603.55
|
399798.09
|
399687.42
|
|
Log Likelihood
|
-210279.95
|
-199855.60
|
-199762.25
|
|
Num. obs.
|
54769
|
52148
|
52148
|
|
Num. groups: idschool
|
243
|
243
|
243
|
|
Var: idschool (Intercept)
|
2.16
|
2.09
|
2.02
|
|
Var: Residual
|
9554.61
|
9597.23
|
9563.16
|
|
p < 0.001, p < 0.01, p < 0.05
|
<!DOCTYPE HTML PUBLIC “-//W3C//DTD HTML 4.01 Transitional//EN” “
http://www.w3.org/TR/html4/loose.dtd”>
VD: Institutional Trusts
|
|
Model 1
|
Model 2
|
Model 3
|
Model 4
|
Model 5
|
|
(Intercept)
|
62.99***
|
43.37***
|
14.16***
|
12.45***
|
13.68***
|
|
|
(0.28)
|
(0.52)
|
(1.26)
|
(1.29)
|
(1.30)
|
|
civic_knowledge
|
-0.03***
|
-0.01***
|
0.05***
|
0.04***
|
0.04***
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
|
l_autgov
|
|
0.23***
|
0.81***
|
0.75***
|
0.72***
|
|
|
|
(0.01)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
civic_knowledge:l_autgov
|
|
|
-0.00***
|
-0.00***
|
-0.00***
|
|
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
|
s_hisced
|
|
|
|
0.02
|
-0.02
|
|
|
|
|
|
(0.03)
|
(0.03)
|
|
s_homelit
|
|
|
|
-0.29***
|
-0.25***
|
|
|
|
|
|
(0.04)
|
(0.04)
|
|
s_gender
|
|
|
|
-0.68***
|
-0.74***
|
|
|
|
|
|
(0.09)
|
(0.09)
|
|
s_poldisc
|
|
|
|
0.10***
|
0.11***
|
|
|
|
|
|
(0.00)
|
(0.00)
|
|
factor(time)2016
|
|
|
|
|
0.02
|
|
|
|
|
|
|
(0.10)
|
|
factor(idcountry)170
|
|
|
|
|
-0.31
|
|
|
|
|
|
|
(0.19)
|
|
factor(idcountry)214
|
|
|
|
|
2.67***
|
|
|
|
|
|
|
(0.29)
|
|
factor(idcountry)320
|
|
|
|
|
-3.39***
|
|
|
|
|
|
|
(0.37)
|
|
factor(idcountry)484
|
|
|
|
|
-0.25
|
|
|
|
|
|
|
(0.18)
|
|
factor(idcountry)600
|
|
|
|
|
-0.39
|
|
|
|
|
|
|
(0.46)
|
|
factor(idcountry)604
|
|
|
|
|
-2.47***
|
|
|
|
|
|
|
(0.24)
|
|
AIC
|
415561.93
|
412156.29
|
411539.04
|
398745.91
|
398345.59
|
|
BIC
|
415597.36
|
412200.57
|
411592.17
|
398834.17
|
398495.62
|
|
Log Likelihood
|
-207776.96
|
-206073.15
|
-205763.52
|
-199362.96
|
-199155.79
|
|
Num. obs.
|
51954
|
51794
|
51794
|
50281
|
50281
|
|
Num. groups: idschool
|
243
|
243
|
243
|
243
|
243
|
|
Var: idschool (Intercept)
|
2.12
|
1.63
|
1.59
|
1.53
|
1.49
|
|
Var: Residual
|
13434.24
|
12896.01
|
12739.18
|
12669.48
|
12565.38
|
|
p < 0.001, p < 0.01, p < 0.05
|
#Plot
library(sjPlot)
plot2 <- plot_model(m8b, type = "pred", terms = c("civic_knowledge[300, 400, 500, 600, 700]", "l_autgov[30, 40, 50, 60 ,70]"), colors = 'bw', axis.title = c("Civic Knowledge", "Students' trust in civic institutions"), title = c(""), legend.title = "Support authoritarianism")
plot2

quartz_off_screen 2
#Plot
plot3 <- plot_model(m8b, type = "pred", terms = c("l_autgov[30, 40, 50, 60 ,70]", "civic_knowledge[300, 400, 500, 600, 700]"), colors = 'bw', axis.title = c("Support for authoritarian practices", "Students' trust in civic institutions"), title = c(""), legend.title = "Civic knowledge")
plot3

quartz_off_screen 2